L05 Annotation & Positioning

Data Visualization (STAT 302)

Overview

The goal of this lab is to explore methods for annotating and positioning with ggplot2 plots. This lab also utilizes scale_* to a greater degree which is part of our next reading. In fact, students may find going through/reading chapter 11 Colour scales and legends useful.

Datasets

We’ll be using the blue_jays.rda, titanic.rda, Aus_athletes.rda, and tech_stocks.rda datasets.

Code

# Load package(s)
library(tidyverse)
library(patchwork)

# Load data
load("data/blue_jays.rda")
load("data/titanic.rda")
load("data/Aus_athletes.rda")
load("data/tech_stocks.rda")

Exercises

Complete the following exercises.

Exercise 1

Using the blue_jays.rda dataset, recreate the following graphic as precisely as possible.

Hints:

• Transparency is 0.8
• Point size 2
• Create a label_info dataset that is a subset of original data, just with the 2 birds to be labeled
• Shift label text horizontally by 0.5
• See ggplot2 textbook 8.3 building custom annotations
• Annotation size is 4
• Classic theme

Code

xrng = range(blue_jays$Mass)
yrng = range(blue_jays$Head)
caption = "Head length versus body mass for 123 blue jays"

label_info = blue_jays %>% 
  filter(BirdID %in% c("1142-05914","702-90567"))

ggplot(blue_jays, mapping = aes(x= Mass, y = Head, color = KnownSex)) +
  geom_point(size = 2, alpha = 0.8, show.legend = FALSE) +
  geom_text(label_info, mapping = aes(label = KnownSex), nudge_x = 0.5, show.legend = FALSE) +
  annotate(geom = "text",x = xrng[1], y = yrng[2], label = caption, hjust = 0, vjust = 1, size = 4) +
  labs(x = "Body mass (g)", y = "Head length (mm)") +
  theme_classic()

Exercise 2

Using the tech_stocks dataset, recreate the following graphics as precisely as possible. Use the column price_indexed.

Plot 1

Hints:

• Create a label_info dataset that is a subset of original data, just containing the last day’s information for each of the 4 stocks
• serif font
• Annotation size is 4

#stock price by date

Code

caption <- paste("Stock price over time for four major tech companies")
caption_print <- paste(strwrap(caption, 40), collapse = "\n")  
label_info <- tech_stocks %>%
  ungroup() %>%
  arrange(desc(date)) %>% 
  distinct(company, .keep_all = TRUE) 

xrng <- range(tech_stocks$date)
yrng <- range(tech_stocks$price_indexed)
tech_stocks <- tech_stocks %>%
  ungroup()

ggplot(tech_stocks, aes(date, price_indexed)) +
  geom_line(aes(color = company)) +
  xlab(NULL) +
  ylab("Stock price, indexed") +
  annotate("text",
           x = xrng[1], 
           y = yrng[2],
           label = caption,
           hjust = 0,
           vjust = 1,
           family = "serif",
           size = 4) +
  geom_text(data = label_info, aes(label = company)) + 
  theme_minimal()

Plot 2

Hints:

• Package ggrepel
  • box.padding is 0.6
  • Minimum segment length is 0
  • Horizontal justification is to the right
  • seed of 9876
• Annotation size is 4
• serif font

Code

ggplot(tech_stocks, aes(date, price_indexed)) +
  geom_line(aes(color = company)) +
  xlab(NULL) +
  ylab("Stock price, indexed") +
  annotate("text",
           x = xrng[1], 
           y = yrng[2],
           label = caption,
           hjust = 0,
           vjust = 1,
           family = "serif",
           size = 4) +
  ggrepel::geom_text_repel(data = label_info, aes(label = company),
                  box.padding = 0.6,
                  min.segment.length = 0,
                  hjust = 1,
                  seed = 9876) +
  theme_minimal()

Exercise 3

Using the titanic.rda dataset, recreate the following graphic as precisely as possible.

Hints:

• Create a new variable that uses died and survived as levels/categories
• Hex colors: #D55E00D0, #0072B2D0 (no alpha is being used)

Code

ggplot(titanic, aes(sex, fill = sex)) +
  geom_bar() +
  facet_grid(factor(survived, labels = c("died", "survived"))
    ~class) +
  scale_fill_manual(values = c("#D55E00D0", "#0072B2D0")) +
  theme_minimal() +
  theme(legend.position = "none")

Exercise 4

Use the athletes_dat dataset — extracted from Aus_althetes.rda — to recreate the following graphic as precisely as possible. Create the graphic twice: once using patchwork and once using cowplot.

Code

# Get list of sports played by BOTH sexes
both_sports <- Aus_athletes %>%
  # dataset of columns sex and sport 
  # only unique observations
  distinct(sex, sport) %>%
  # see if sport is played by one gender or both
  count(sport) %>%
  # only want sports played by BOTH sexes
  filter(n == 2) %>%
  # get list of sports
  pull(sport)

# Process data
athletes_dat <- Aus_athletes %>%
  # only keep sports played by BOTH sexes
  filter(sport %in% both_sports) %>%
  # rename track (400m) and track (sprint) to be track
  # case_when will be very useful with shiny apps
  mutate(
    sport = case_when(
      sport == "track (400m)" ~ "track",
      sport == "track (sprint)" ~ "track",
      TRUE ~ sport
      )
    )

Hints:

• Build each plot separately
• Bar plot: lower limit 0, upper limit 95
• Bar plot: shift bar labels by 5 units and top justify
• Bar plot: label size is 5
• Bar plot: #D55E00D0 & #0072B2D0 — no alpha
• Scatterplot: #D55E00D0 & #0072B2D0 — no alpha
• Scatterplot: filled circle with “white” outline; size is 3
• Scatterplot: rcc is red blood cell count; wcc is white blood cell count
• Boxplot: outline #D55E00 and #0072B2; shading #D55E0040 and #0072B240
• Boxplot: should be made narrower; 0.5
• Boxplot: Legend is in top-right corner of bottom plot
• Boxplot: Space out labels c("female ", "male")
• Boxplot: Legend shading matches hex values for top two plots

Code

bar_labels <- athletes_dat %>% count(sex)
bar_plot <- ggplot(athletes_dat, aes(sex, fill = sex), size = 5) + 
  geom_bar() +
  geom_text(
    data = bar_labels,
    mapping = aes(y=n, label = n),
    size = 5,
    vjust = 1,
    nudge_y = -5
  ) +
  scale_y_continuous(name = "number",
                     limits = c(0, 95),
                     expand = c(0, 0)
  ) + 
  scale_x_discrete(labels = c("female", "male")) +
  scale_fill_manual(
    values = c("#D55E00D0", "#0072B2D0"),
    guide = "none"
  ) + 
  theme_minimal()

scatter_plot <- ggplot(athletes_dat, aes(rcc, wcc, fill=sex)) +
  geom_point(
      shape = 21,
      color = "white",
      size = 3
    ) + 
  scale_fill_manual(values = c("#D55E00D0", "#0072B2D0"), guide = "none") + 
  labs("RBC Count", "WBC count") + 
  theme_minimal()

box_plot <- ggplot(athletes_dat, aes(sport, pcBfat, fill = sex, color = sex)) +
  geom_boxplot(width = 0.5) +
  scale_color_manual(
      name = NULL,
      labels = c("female    ", "male"),
      values = c("#D55E00", "#0072B2")
  ) +
  scale_fill_manual(
      name = NULL,
      values = c("#D55E0040", "#0072B240"),
      guide = "none"
  ) + 
  xlab(NULL) + 
  ylab("% body fat") + 
  theme_minimal() + 
  theme(
      legend.position = c(1, 1),
      legend.justification = c(1, 1),
      legend.direction = "horizontal"
  ) +
  guides(color = guide_legend(
    ovverride.aes = list(color = NA)
    )
  )

Using patchwork

Code

(bar_plot + scatter_plot) / box_plot

Using cowplot

Use cowplot::plot_grid() to combine them.

Code

cowplot::plot_grid(
    cowplot::plot_grid(bar_plot, scatter_plot, nrow = 1), box_plot, nrow = 2)

Exercise 5

Create the following graphic using patchwork.

Hints:

• Use plots created in Exercise 4
• inset theme is classic
  • Useful values: 0, 0.45, 0.75, 1
• plot annotation "A"

Code

scatter_plot +
  patchwork::inset_element(
    bar_plot + theme_classic(), 
    left = 0.75, 
    right = 1, 
    bottom = 0, 
    top = 0.45
  ) +
  patchwork::plot_annotation(tag_levels = 'A')